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Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at hf.co/hardware/habana.

Stable Diffusion HPU configuration

This model only contains the GaudiConfig file for running Stable Diffusion 1 (e.g. CompVis/stable-diffusion-v1-4) or Stable Diffusion 2 (e.g. stabilityai/stable-diffusion-2) on Habana's Gaudi processors (HPU).

This model contains no model weights, only a GaudiConfig.

This enables to specify:

  • use_habana_mixed_precision: whether to use Habana Mixed Precision (HMP)
    • hmp_opt_level: optimization level for HMP, see here for a detailed explanation
    • hmp_bf16_ops: list of operators that should run in bf16
    • hmp_fp32_ops: list of operators that should run in fp32
    • hmp_is_verbose: verbosity


The GaudiStableDiffusionPipeline (GaudiDDIMScheduler) is instantiated the same way as the StableDiffusionPipeline (DDIMScheduler) in the 🤗 Diffusers library. The only difference is that there are a few new training arguments specific to HPUs.

Here is an example with one prompt:

from optimum.habana import GaudiConfig
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline

model_name = "stabilityai/stable-diffusion-2"

scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")

pipeline = GaudiStableDiffusionPipeline.from_pretrained(

outputs = generator(
    ["An image of a squirrel in Picasso style"],

Check out the documentation and this example for more advanced usage.

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Unable to determine this model’s pipeline type. Check the docs .